Entities such as manufacturers, retailers, service providers, and others generally catalog items such as a product, a service, or a coupon that they make available. A catalog may be stored using a data structure having data elements with multiple dimensions. A dimension may include information known about the particular data element being described so that various data about the object may be ascertained. For example, dimensions such as a category, a location (e.g., a location where a product, service, or coupon is offered by an entity), and/or other information known about the item can be associated with the item. In this manner, using a dimensional data structure, entities may store and retrieve information about the items that they make available.
Typically, the data structures and dimensions of the catalogs vary from one entity to another entity and, in some cases, may be proprietary. As a result, correlating a dimension of a data structure to another dimension of another data structure may be difficult. A resulting problem is that adding an item to a given catalog or determining a category to which the item belongs for a given entity can be a time-consuming and difficult process. For example, a manufacturer wishing to promote a new item across different online and brick-and-mortar retailers may find it difficult to determine how these retailers and other entities categorize the new item because the various retailers have different data structures and catalogs.
Conventionally, such determinations are a manual process by those having knowledge of the data structures used by an entity. Thus, what is needed is to automatically translate a dimension of one data structure to another data structure. More particularly, what is needed is to be able to determine a category of an item from various entities that use different data structures for storing the category of the item. These and other drawbacks exist.